class_docstr: Flavor backend implementation for the generic R models.
  Predict and serve locally models with 'crate' flavor.
class_docstr_tok: ["Flavor","backend","implementation","for","the","generic","R","models",".","Predict","and","serve","locally","models","with","'crate","'","flavor","."]
docstr: Generate R model locally.
  
  NOTE: The `enable_mlserver` parameter is there to comply with the
  FlavorBackend interface but is not supported by MLServer yet.
  https://github.com/SeldonIO/MLServer/issues/183
docstr_tok: ["Generate","R","model","locally",".","NOTE",":","The","`","enable_mlserver","`","parameter","is","there","to","comply","with","the","FlavorBackend","interface","but","is","not","supported","by","MLServer","yet",".","https",":","\/\/github.com\/SeldonIO\/MLServer\/issues\/183"]
code: def serve(
      self,
      model_uri,
      port,
      host,
      timeout,
      enable_mlserver,
      synchronous=True,
      stdout=None,
      stderr=None,
  ):
      """
      Generate R model locally.
  
      NOTE: The `enable_mlserver` parameter is there to comply with the
      FlavorBackend interface but is not supported by MLServer yet.
      https://github.com/SeldonIO/MLServer/issues/183
      """
      if enable_mlserver:
          raise Exception(
              "The MLServer inference server is not yet supported in the R backend."
          )
  
      if timeout:
          _logger.warning(
              "Timeout is not yet supported in the R backend."
          )
  
      if not synchronous:
          raise Exception(
              "RBackend does not support call with synchronous=False"
          )
  
      if stdout is not None or stderr is not None:
          raise Exception(
              "RBackend does not support redirect stdout/stderr."
          )
  
      model_path = _download_artifact_from_uri(model_uri)
      command = "mlflow::mlflow_rfunc_serve('{}', port = {}, host = '{}')".format(
          quote(model_path), port, host
      )
      _execute(command)
  
file_path: mlflow/mlflow/rfunc/backend.py
imports: import logging
  import os
  import re
  import subprocess
  import sys
  from mlflow.exceptions import MlflowException
  from mlflow.models import FlavorBackend
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.string_utils import quote
label_desc: Use image node_id 4 for calling the RFuncBackend obj's underlying member method code with example usage: obj.serve(model_uri, port, host, timeout, enable_mlserver, synchronous, stdout, stderr) without return types